The proliferation of digital cameras, image scanning equipment, and graphics software has enabled business and home consumers to be able to perform imaging jobs for various purposes more readily than in the past. For example, home users are able to use such equipment and software to obtain digital images that may be used for everyday purposes, such as school projects for both children and adults, the creation of greeting cards, family photo albums, etc. In addition, both small and large business users are able to employ such equipment to obtain image for use in advertising brochures, internal and external presentations and other documents, etc.
Various software products allow the manipulation of such digital images in order to achieve the desired and result, an image which may be printed using readily available imaging apparatus, such as inkjet printers, electrophotographic printers, and all-in-one units that are capable of performing multiple types of imaging jobs, such as printing, copying, scanning, and faxing. Such software products may be stand alone products created by various software manufacturers, or may be part of software bundle packaged with the imaging apparatus.
Once the images are created or otherwise obtained, they are often stored, for example, in a digital image library. In order to search for a particular image in a digital image library, the user may simple look through all unit the desired image is found, which is a cumbersome process. Where the image library has been organized into various categories and subsets of categories, it may be somewhat easier for the user to find the particular image, although the search may still be cumbersome, especially if the image library is a large one.
In order to aid a user in finding a particular image, various software products include a search feature that searches for particular image characteristics based on a conventional histogram. Images are made up of pixels of information. Each pixel in a color image includes at least 3 channels, for example, red, green, and blue. For each pixel there is a relating quantity of these 3 color channels with a black pixel being [0,0,0] and white being [255,255,255]. Some of the pixel information from the image is used to create the conventional histograms used by the image search engine.
A conventional histogram is the graphical representation of an image wherein the quantities of each channel are mapped out. These different channels are mapped individually. A conventional histogram is a graph where the x-axis represents the particular channel levels with the leftmost and having a value of 0 and the rightmost end having a value of 255.For each channel value the quantity of pixel with that value is graphed on the histogram. Each channel is graphed separately.
However, such conventional histograms are limited in the amount of data that may be used to differentiate between different images. For example, depending on the nature of the images, two disparate images may have the same histogram, such as where the quantity of image pixels having various colors is the same as between the two images, although in the one of the image, the distribution of pixels across the image “canvas” is entirely different than the distribution of pixels in the other image. Hence, the search engine's analyzing of the two different images would yield the false result that the images are similar or the same.
What is needed in the art is an improved method for analyzing an image.